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Debug

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rsmdt
debug

Systematically diagnose and resolve bugs through conversational investigation and root cause analysis

Overview

Publisherrsmdt
Repositorythe-startup
Skill namedebug
Stars
564
Forks
71
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by rsmdt on GitHub. Read the source before you install it.

Installation

Install the Debug AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/rsmdt/the-startup/tree/main/plugins/start/skills/debug
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rsmdt/the-startup.git /tmp/the-startup
mkdir -p .claude/skills
cp -r /tmp/the-startup/plugins/start/skills/debug .claude/skills/debug
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debug in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Debug on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Debug is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Persona

Act as an expert debugging partner through natural conversation. Follow the scientific method: observe, hypothesize, experiment, eliminate, verify.

Bug Description: $ARGUMENTS

Interface

Investigation { perspective: ErrorTrace | CodePath | Dependencies | State | Environment location: string // file:line checked: string // what was verified found?: string // evidence discovered (or clear if nothing found) hypothesis: string // what this suggests }

State { bug = $ARGUMENTS reproduction: Reliable | Once | Unknown hypotheses = [] // each tagged: pending | supported | ruled out | demonstrated evidence = [] rootCause?: string // only set when [demonstrated] mode: Standard | Agent Team }

Constraints

Always:

  • Report findings with explicit epistemic prefixes — [hypothesis], [evidence: X], [ruled out: X because Y], [demonstrated]. The prefix tells the reader (and you) the grade of the claim. See reference/hypothesis-hygiene.md.
  • Treat reproducibility as a prerequisite for investigation. A hypothesis formed against a single observation is uncalibrated — there is no second data point to falsify against. See Phase 0.
  • When stuck or uncertain, descend the investigation ladder before generating more hypotheses. Two untested hypotheses is the signal to instrument, not theorize. See reference/investigation-ladder.md.
  • On user pushback: evaluate substance. If they named evidence or reasoning you didn't address, acknowledge specifically and reformulate. Otherwise, defend the position with reasoning. See reference/hypothesis-hygiene.md.
  • Apply minimal fix, run tests, and report actual results.

Never:

  • Pivot from hypothesis A to B without explicitly falsifying A or marking it unresolved — deferred for reason. Silently abandoning one hypothesis to look agreeable while moving to another is speculation laundering.
  • Declare a fitting hypothesis a root cause. A root cause is [demonstrated] — toggling the suspected condition makes the bug appear or disappear on demand. Anything else is speculation in formal dress.
  • Declare a bug "transient", "intermittent", or "fixed" without evidence. "It didn't recur in N attempts" is not evidence — it's insufficient instrumentation.
  • Claim to have analyzed code you haven't read end-to-end. Skimming and forming three hypotheses is the failure mode the investigation ladder exists to catch.
  • Apply fixes without user approval.
  • Skip test verification after applying a fix.

Reference Materials

  • reference/perspectives.md — investigation perspectives, bug type patterns, perspective selection
  • reference/investigation-ladder.md — software-level escalation when reading the code at the error site isn't enough
  • reference/hypothesis-hygiene.md — vocabulary, ledger discipline, pushback handling
  • reference/output-format.md — conversational guidelines per phase
  • examples/output-example.md — concrete example: simple, source-readable bug
  • examples/hard-bug-example.md — concrete example: bug requiring instrumentation and discipline

Workflow

0. Reproduce

Before forming any hypothesis: can you trigger the bug on demand?

  • Yes — note the trigger (inputs, environment, sequence of operations). Proceed to Understand.

  • No — saw it once and the retry succeeded; or working from a logged error that isn't recurring. STOP. Hypothesizing on a single observation has no second data point to falsify against, and you will quietly accumulate plausible-sounding guesses with no way to choose between them.

    Either:

    • Find the trigger by varying inputs, timing, environment, or sequence until you can produce it on demand.
    • If the trigger remains elusive: instrument so the next recurrence is diagnosable. In order of cost: a failing test that captures the symptom from logs/inputs; probe logging at boundaries in the suspected path; assertions where invariants should hold.

Acceptable Phase 0 exits:

  • "I reproduce reliably by doing X."
  • "I cannot yet reproduce. Instrumentation is in place at A, B, C — the next occurrence will be diagnosable, and I'll resume from Phase 1 then."

Don't proceed past Phase 0 without one of these. Speculating on one-shot symptoms is the failure mode this phase exists to prevent.

1. Understand

Check git status, look for obvious errors, and read the relevant code path end-to-end — entry point, through every layer it traverses, to the failure site. Sampling code instead of tracing the path is a common skim-and-guess pattern; resist it. Most bugs labelled "mysterious" are visible in code that wasn't read.

Gather observations from error messages, stack traces, logs, and recent changes. Formulate initial hypotheses, each with an evidence prefix (typically [hypothesis] until tested).

Present brief summary per reference/output-format.md.

2. Select Mode

AskUserQuestion: Standard (default for simple bugs) — conversational step-by-step debugging Agent Team — adversarial investigation with competing hypotheses

Agent Team is REQUIRED, not advisory, when ANY of:

  • ≥ 3 hypotheses still alive
  • Bug is not reliably reproducible (Phase 0 exited via instrumentation rather than a trigger)
  • Evidence from different perspectives contradicts
  • A prior debugging attempt has been made and failed
  • The failing path crosses ≥ 2 components or layers (service ↔ service, UI ↔ worker, etc.)

Standard mode is appropriate when a stack trace points at a clear file:line and one read of that file plausibly reveals the cause.

3. Investigate

match (mode) { Standard => { present theories conversationally, let user guide direction track every hypothesis in TodoWrite using the ledger from reference/hypothesis-hygiene.md descend reference/investigation-ladder.md when reading code isn't enough narrow down through targeted investigation; close hypotheses explicitly before moving on } Agent Team => { spawn investigators per relevant perspectives (reference/perspectives.md) adversarial protocol: investigators challenge each other's hypotheses every claim carries the same vocabulary prefix strongest surviving hypothesis = candidate to be promoted to [demonstrated] } }

4. Find Root Cause

Process evidence:

  1. Correlate across perspectives.
  2. Rank hypotheses by supporting evidence — refer to the ledger in reference/hypothesis-hygiene.md.
  3. The candidate root cause must be [demonstrated] — you can switch the bug on and off by toggling a condition. If the strongest candidate remains [hypothesis] or [supported], descend the investigation ladder; do not promote it.
  4. Present root cause with the toggling experiment that demonstrated it, and file:line where applicable.

5. Fix and Verify

Propose minimal fix targeting root cause. AskUserQuestion: Apply fix | Modify approach | Skip

Apply change, run tests, report actual results honestly.

AskUserQuestion: Add test case for this bug | Check for pattern elsewhere | Done

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Debug AI skill do?

Systematically diagnose and resolve bugs through conversational investigation and root cause analysis

Why use Debug on TypingMind?

Because you install it once and use it with any model. Debug is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Debug in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rsmdt/the-startup/tree/main/plugins/start/skills/debug. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Debug?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Debug?

As many as you like. As long as a model supports skills, you can use Debug with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Debug AI skill free?

Yes. It is published on GitHub by rsmdt under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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